Having a context sensitive user interface which can automatically choose from a multiplicity of options based on the current or previous state(s) of a program operation can be found in current graphical user interface. For example: Clicking on a text document automatically opens the document in a word processing environment. The user does not have to specify what type of program to use to open the file. Program files and their shortcuts (i.e. executable files) can be associated with certain type of files, e.g. text document, and are automatically run by the operating system when the user selects or double clicks the file. Similarly, the user-interface may also provide context sensitive feedback, such as changing the appearance and/or color of the mouse pointer or cursor. In addition, context sensitive feedback may also be used in video games where it change a button's function based on a player who is in a certain position or a place and needs to interact with an object.
Relational databases are currently the predominant choice in storing data like financial records, medical records, personal information, manufacturing and logistical data. Nowadays large-scale data or information processing can involve various types of collection, extraction, warehousing, analysis and statistics. For example, organizing and matching data by using some common characteristics found within the data set would result in new groups of data that can organized and are easier for many people to understand, search, index and manipulate.
By describing the contents and context of data files, the quality of processing the original data files can be greatly increased. For example, a webpage may include metadata specifying what language was used in writing its code, what tools were used to create it, and where to go for more on the subject, higher-level concepts that describe the data. Thus allowing browsers to automatically improve the experience of users. The results of any large-scale data processing can be an extensive set of meta-data, data, and relationships that may be used in a search engine, for example, to provide a possible set of related information to a term that is used in a search query. For example, search engines have used and generated enormous amount of data and metadata that is used to provide links to content that may be of possible interest to a user based on what the user is searching for.
As stored digital information has increased tremendously in size, the ability of a user to use effectively personal data, corporate data, or publically available data has also increased many folds although it still falls short of the potential of reasoning about the large amount of data that is available and continues to grow at an astounding pace. Therefore, there exists a need to more effectively use and reason about the data, and with more a richer augmented user experience while reading, writing, searching, or using digital data information.
Large amount of data can be stored using various types of relational databases, network based storage, or cloud based storage. These are but some examples of predominant choices in storing data and information like financial records, medical records, personal information, manufacturing and logistical data. Nowadays large scale data or information processing can involve various types of collection, extraction, warehousing, analysis and statistics. For example, organizing and matching data by using some common characteristics found within the data set would result in a new groups of data that can be organized and are easier, for many people, to understand, search, index and manipulate.
As stored digital information has increased tremendously in terms of size or amount of data information, the ability of a user to use effectively personal data, corporate data, or publically available data has also increased many folds. Additional problems are encountered in finding relevant data for a user's needs. Knowledge discovery platform, systems as described in related patent applications can be used to generate augmented knowledge using such large scale data that meet the needs of a user. The augmented knowledge provided to the user can be highly relevant to another user or another knowledge discovery system. However, the other user or system may have certain distinct criterions, characteristics, preferences or interests that are different from the first user.
Thus, an increase in accuracy and efficiency can be achieved by benefiting from using the augmented knowledge already obtained for a first user and by a regenerated or modified augmented content and knowledge tailored to a second user's interest, profile, or preferences. Therefore, there exists a need for a knowledge discovery system that can leverage the knowledge discovered for a first user to provide augmented knowledge and/or newly discovered or augmented knowledge based on a second user's preferences or interests.